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Factors Affecting Probability Matching Behavior

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Factors Affecting Probability Matching Behavior Factors Affecting Probability Matching Behavior Jie Gao Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy under the Executive Committee of the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2013 © 2013 Jie Gao All rights reserved ABSTRACT FACTORS AFFECTING PROBABILITY MATCHING BEHAVIOR Jie Gao In life, people commonly face repeated decisions under risk or uncertainty. While normative economic models assume that people tend to make choices that maximize their expected utility, suboptimal behavior – in particular, probability matching – is frequently observed in research on repeated decisions. Probability matching is the tendency to match prediction probabilities of each outcome with the observed outcome probabilities in a random binary prediction task. For example, when people are faced with making with a sequence of predictions, such as repeatedly predicting the outcome of rolling a die with four sides colored green and two sides colored red, most people allocate about two-thirds of their predictions to green, and one-third to red. The optimal strategy, referred to as maximizing, would be to choose the outcome with the higher probability in every trial in the prediction task. Various causes for probability matching have been proposed during the past several decades. Here it is proposed that implicit adoption of a perfect prediction goal by decision makers might tend to elicit probability matching behavior. Thus, one factor that might affect the prevalence of probability matching behavior (investigated in Studies 1 and 2) is the type of performance goal. The manipulation in Study 1 contrasted single-trial prediction with prediction of four-trial sequences, which it is hypothesized might create an implicit perfect prediction goal for the sequence. In Study 2, three levels of goal were explicitly manipulated for each sequence: a perfect prediction goal, an 80% correct goal, and a 60% correct goal. In both studies it was predicted that more matching behavior would be observed for those who have a goal of perfect prediction than those who have a more reasonable (lower) goal. The results of both studies, conducted in an online worker marketplace, supported the goal-level hypothesis. The second factor proposed to affect the prevalence of probability matching is the type of conceptual schema describing the events to be predicted: independent events or complementary events. Study 3 investigated the effects of schema type and abstraction level of context on matching or maximizing behavior. Three abstraction levels of stories were included: abstract, concrete random devices, and real-world stories. The main hypothesis was that when the two options to be predicted are independent events, less matching and more maximizing behavior should be observed. Data from Study 3 supported the hypothesis that independent events tend to elicit more maximizing behavior. No effects of abstraction level were observed. TABLE OF CONTENTS LIST OF TABLES III LIST OF FIGURES V ACKNOWLEDGEMENTS VII CHAPTER I INTRODUCTION 1 Research Questions 6 CHAPTER II REVIEW OF LITERATURE 9 Overview of Probability Matching Research 9 1. Reinforcement Learning 9 2. Expectation Matching 10 3. Dual-Systems Account 11 4. Pattern-Search Account 13 5. The Role of Unrealistically High Goals 14 6. Summary and Discussion of the Previous Literature 15 Misconception of Randomness 17 Description-based vs. Experience-based Decision Making 19 Goals and Decision Making 22 Pragmatic Reasoning Schemas 25 CHAPTER III STUDY ONE 27 Introduction 27 Method 28 Results 34 Discussion 42 CHAPTER IV STUDY TWO 46 Introduction 46 Method 47 i Results 51 Discussion 54 CHAPTER V STUDY THREE 55 Introduction 55 Method 59 Results 64 Discussion 68 CHAPTER VI GENERAL DISCUSSION 72 REFERENCES 77 APPENDIX A. STUDY 1 87 1. Probability of choosing the more likely outcome 87 2. Earned Payoff information 88 3. ANOVA Tables 89 4. Analysis of Individual Differences 90 APPENDIX B. STUDY 2 92 1. Probability of choosing the more likely outcome 92 2. Earned Payoff information 92 3. ANOVA Tables 93 4. Survey Analysis 93 APPENDIX C. STUDY 3 98 1. Earned Payoff information 98 2. ANOVA Table 98 ii LIST OF TABLES Table 1. Comparison of Expectation Matching and Win-Stay-Lose-Shift, summarized from Otto, Taylor and Markman (2011) 16 Table 2 Comparison of Pattern Search and Win-Stay-Lose-Shift, summarized from Gaissmaier and Schooler (2008) 16 Table 3. Illustration of Counterbalanced Conditions in Study 1 29 Table 4 Number of Participants in Each Condition of Study 1 30 Table 5 Study Design and Number of Participants in Each Condition of Study 2 49 Table 6 Descriptive statistics for the matching variable for different goal-level groups 53 Table 7 Conditions and stories in Study 3 64 Table 8 Descriptive Statistics for Each Condition 65 Table 9. Bonus Information for Study 1 (Earned and Expected) 89 Table 10. ANOVA Table for No-Bias Condition (DV=Matching) 89 Table 11. ANOVA Table for Bias Condition (DV=Matching) 89 Table 12. ANOVA Table for Bias Condition (DV=Maximizing) 90 Table 13. Individual Differences: Frequencies of Participants Showing Various Patterns of Matching Behavior 91 Table 14. Individual Differences: Frequencies of Participants Showing Various Patterns of Maximizing Behavior 91 Table 15 Expected Score Calculation Results 93 Table 16. Payoff Information for Study 2 (Earned and Expected Bonus) 93 Table 17. ANOVA Table for Matching 93 Table 18. ANOVA Table for Maximizing 93 iii Table 19. Distribution of Coded Responses to Survey Question 1 in Study 2 94 Table 20. Distribution of Responses to Survey Question 2a in Study 2 94 Table 21. Distribution of Responses to Survey Question 2b in Study 2 95 Table 22. Distribution (Frequencies) of Responses to Survey Question 3 in Study 2 96 Table 23. Distribution of Responses (Mean estimated 96 Table 24. Distribution of Responses to Survey Question 5 in Study 2 97 Table 25. Payoff Information (Earned and Expected) for Different Types of Context 98 Table 26. Payoff Information (Earned and Expected) for Different Schema Types 98 Table 27. ANOVA Table for Predictions 98 iv LIST OF FIGURES Figure 1 Interface for Single Trial Prediction Tasks 32 Figure 2 Interface for Four-trial Set Prediction Tasks 33 Figure 3 The proportion of participants using probability matching strategy, for no-bias conditions (single-trial and sequence conditions) 36 Figure 4 The proportion of participants using probability matching strategy, for bias conditions (single-trial and sequence conditions) 37 Figure 5 The proportion of using maximizing strategy, for bias conditions only (single-trial and sequence conditions) 39 Figure 6 The distribution of individuals’ probabilities over the first 20 trials (left panels) and last 20 trials (right panels) of choosing the red light in the no-bias (50%) condition. 40 Figure 7 The distribution of individuals’ probabilities over the first 20 trials (left panels) and last 20 trials (right panels) of choosing the more likely outcome in the bias (75%) conditions. 41 Figure 8 The proportion of participants using a matching strategy across sets in the three goal conditions 52 Figure 9 Error-bar plot for mean of matching variables 53 Figure 10 The proportion of participants using maximizing strategy across sets in the three goal conditions 54 Figure 11 Profile plot of the probability of choosing the more likely outcome (Schema Type x Sets) 66 Figure 12 Profile plot of the probability of choosing the more likely outcome (Schema Type x Context Level) 67 v Figure 13 Profile plot of the probability of choosing the more likely outcome, under the different contexts 68 Figure 14 The probability of predicting the more likely outcome for bias conditions, and the probability of choosing Red light for no-bias conditions 88 Figure 15 The probability of choosing the more likely event across the 5-trial sets in the three goal conditions 92 vi ACKNOWLEDGEMENTS There are many people I wish to thank for their contributions to this dissertation. First, I want to thank my advisor, Professor James Corter, for his guidance, support, and encouragement during my wonderful journey of doctoral study. I also want to gratefully acknowledge the financial support of the Department of Human Development in Teachers College of Columbia University. In addition, I wish to thank Professor Joanna P. Williams, Matthew S. Johnson, Elissa L. Perry, and Eric Johnson for their helpful feedback and advice. I am grateful to HuiYun Tseng, Yichun Chen and Yunjin Rho for their invaluable advice as senior follow students. Additionally, I would like to thank Yuan Zhang, Daoquan Li, Qing Xia, Wei Huang, Shijiao Jiang, Debbie and Eric Carson and their family for their enduring friendship, emotional support, and laughter throughout graduate school. Finally, I want to express my gratitude to my entire family, and especially to my husband, Yu Li, for their love, encouragement and faith in me. vii Factors Affecting Probability Matching 1 Chapter I Introduction Probability matching is the tendency to match prediction probabilities of each outcome with the observed outcome probabilities in a random binary prediction task (Fiorina, 1971; Fantino & Esfandiari, 2002; Shanks, Tunney, & McCarthy, 2002). This is non-optimal or “irrational” behavior. For example, when people are faced with a sequence of prediction tasks (repeated trials), such as predicting the outcome of rolling a die with four sides colored green and two sides colored red, most people allocate about two-thirds of their predictions on green, and one-third on red. This probability-matching prediction strategy is sub-optimal; the optimal strategy is maximizing, meaning to choose the outcome with the higher probability in every trial in the prediction task. In real-life, people do face repeated decisions under risk or uncertainty. For example, in the stock market, people are making repeated decisions to buy or sell a certain stock whose price may increase or decrease; companies make repeated decisions about hiring students who graduated with certain degrees or from certain colleges, believing that these qualifications affect the probability of success of the new employee.
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